Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson’s disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platform’s robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinson’s disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

Post-traumatic stress disorder (PTSD) can develop following severe trauma, but the extent to which genetic and environmental risk factors contribute to individual clinical outcomes is unknown. Here, we compared transcriptional responses to hydrocortisone exposure in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and peripheral blood mononuclear cells (PBMCs) from combat veterans with PTSD (n = 19 hiPSC and n = 20 PBMC donors) and controls (n = 20 hiPSC and n = 20 PBMC donors). In neurons only, we observed diagnosis-specific glucocorticoid-induced changes in gene expression corresponding with PTSD-specific transcriptomic patterns found in human postmortem brains. We observed glucocorticoid hypersensitivity in PTSD neurons, and identified genes that contribute to this PTSD-dependent glucocorticoid response. We find evidence of a coregulated network of transcription factors that mediates glucocorticoid hyper-responsivity in PTSD. These findings suggest that induced neurons represent a platform for examining the molecular mechanisms underlying PTSD, identifying biomarkers of stress response, and conducting drug screening to identify new therapeutics.

Drug discovery for Parkinson’s disease (PD) is impeded by the lack of screenable phenotypes in scalable cell models. Here we present a novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and carefully matched healthy controls, generating the largest publicly available Cell Painting dataset to date. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated unbiased deep embeddings from each image, and applied these to train machine learning models to detect morphological disease phenotypes. Interestingly, our models captured individual variation by identifying specific cell lines within the cohort with high fidelity, even across different batches and plate layouts, demonstrating platform robustness and sensitivity. Importantly, our models were able to confidently separate LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for PD modeling and drug screening applications.

The COVID-19 pandemic has been identified as a cause of a sudden increase in puppy acquisition from breeders and shelters. During lockdown, opportunities to expose developing puppies to a variety of novel humans and animals were greatly diminished. A visit to the veterinarian for vaccinations represented one of the few essential points of contact. This article discusses possible interventions and suggestions a veterinary technician could make to new puppy owners to increase their chances of providing adequate socialization.

Human pluripotent stem cells (hPSCs), which include induced pluripotent stem cells and embryonic stem cells, are powerful tools for studying human development, physiology and disease, including those affecting the retina. Cells from selected individuals, or specific genetic backgrounds, can be differentiated into distinct cell types allowing the modelling of diseases in a dish for therapeutic development. hPSC-derived retinal cultures have already been used to successfully model retinal pigment epithelium (RPE) degeneration for various retinal diseases including monogenic conditions and complex disease such as age-related macular degeneration. Here, we will review the current knowledge gained in understanding the molecular events involved in retinal disease using hPSC-derived retinal models, in particular RPE models. We will provide examples of various conditions to illustrate the scope of applications associated with the use of hPSC-derived RPE models.